Orateur
Description
Ensuring the reliability and resilience of the modern power grid requires models that handle the inherent uncertainty of generation availability and electricity consumption. These models must have sufficiently high spatial and temporal resolution to adequately capture weather variability and provide actionable siting and sizing decisions. A stochastic nodal capacity expansion planning (CEP) model can satisfy these requirements, but the presence of binary and integer variables in the optimization problem introduces challenges of computational scalability. We employ a power transfer distribution factors (PTDF) representation of power flow constraints within a stochastic nodal generation, transmission, and storage CEP model to reduce model size and improve solution time in an iterative approach. We implement a method to address the changes in system topology that occur during the optimization when this type of model is used. The method proposed consistently outperforms the more commonly used b-theta formulation of linear DC power flow in terms of the resulting optimal cost and computational solution time when applied to realistic test systems based on California and South Carolina.
